code: def forward(
      self, x_in: Tuple, h: Optional[torch.Tensor] = None
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      x, _ = x_in
      # data is of size (batch_size, input_length, input_size)
      batch_size = x.shape[0]
  
      # out is of size (batch_size, input_length, hidden_dim)
      out, last_hidden_state = (
          self.rnn(x) if h is None else self.rnn(x, h)
      )
  
      # Here, we apply the V matrix to every hidden state to produce the outputs
      predictions = self.V(out)
  
      # predictions is of size (batch_size, input_length, target_size)
      predictions = predictions.view(
          batch_size, -1, self.target_size, self.nr_params
      )
  
      # returns outputs for all inputs, only the last one is needed for prediction time
      return predictions, last_hidden_state
  
imports: import inspect
  from abc import ABC, abstractmethod
  from typing import Optional, Sequence, Tuple, Type, Union
  import torch
  import torch.nn
  from darts.logging import get_logger, raise_if_not, raise_log
  from darts.models.forecasting.pl_forecasting_module import PLDualCovariatesModule, io_processor
  from darts.models.forecasting.torch_forecasting_model import DualCovariatesTorchModel
  from darts.timeseries import TimeSeries
  from darts.utils.data import DualCovariatesShiftedDataset, TrainingDataset
label_desc: Use image node_id 2 for calling the _RNNModule obj's underlying member method code with example usage: obj.forward(x_in, h) and returns: predictions,  last_hidden_state